Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 15 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 176,893 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 176,883 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 1
## 133 2020-07-11 East of England 1
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 2
## 136 2020-03-01 London 0
## 137 2020-03-02 London 0
## 138 2020-03-03 London 0
## 139 2020-03-04 London 0
## 140 2020-03-05 London 0
## 141 2020-03-06 London 1
## 142 2020-03-07 London 0
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## 145 2020-03-10 London 0
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## 149 2020-03-14 London 13
## 150 2020-03-15 London 9
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## 152 2020-03-17 London 23
## 153 2020-03-18 London 27
## 154 2020-03-19 London 25
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## 157 2020-03-22 London 54
## 158 2020-03-23 London 63
## 159 2020-03-24 London 86
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## 272 2020-03-02 Midlands 0
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## 274 2020-03-04 Midlands 0
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## 277 2020-03-07 Midlands 0
## 278 2020-03-08 Midlands 2
## 279 2020-03-09 Midlands 1
## 280 2020-03-10 Midlands 0
## 281 2020-03-11 Midlands 2
## 282 2020-03-12 Midlands 6
## 283 2020-03-13 Midlands 5
## 284 2020-03-14 Midlands 4
## 285 2020-03-15 Midlands 5
## 286 2020-03-16 Midlands 11
## 287 2020-03-17 Midlands 8
## 288 2020-03-18 Midlands 13
## 289 2020-03-19 Midlands 8
## 290 2020-03-20 Midlands 28
## 291 2020-03-21 Midlands 13
## 292 2020-03-22 Midlands 31
## 293 2020-03-23 Midlands 33
## 294 2020-03-24 Midlands 41
## 295 2020-03-25 Midlands 48
## 296 2020-03-26 Midlands 64
## 297 2020-03-27 Midlands 72
## 298 2020-03-28 Midlands 89
## 299 2020-03-29 Midlands 92
## 300 2020-03-30 Midlands 90
## 301 2020-03-31 Midlands 123
## 302 2020-04-01 Midlands 140
## 303 2020-04-02 Midlands 142
## 304 2020-04-03 Midlands 124
## 305 2020-04-04 Midlands 151
## 306 2020-04-05 Midlands 164
## 307 2020-04-06 Midlands 140
## 308 2020-04-07 Midlands 123
## 309 2020-04-08 Midlands 186
## 310 2020-04-09 Midlands 139
## 311 2020-04-10 Midlands 127
## 312 2020-04-11 Midlands 142
## 313 2020-04-12 Midlands 139
## 314 2020-04-13 Midlands 120
## 315 2020-04-14 Midlands 116
## 316 2020-04-15 Midlands 147
## 317 2020-04-16 Midlands 102
## 318 2020-04-17 Midlands 118
## 319 2020-04-18 Midlands 115
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## 321 2020-04-20 Midlands 107
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## 323 2020-04-22 Midlands 78
## 324 2020-04-23 Midlands 103
## 325 2020-04-24 Midlands 79
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## 334 2020-05-03 Midlands 52
## 335 2020-05-04 Midlands 61
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## 337 2020-05-06 Midlands 59
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## 340 2020-05-09 Midlands 37
## 341 2020-05-10 Midlands 42
## 342 2020-05-11 Midlands 33
## 343 2020-05-12 Midlands 45
## 344 2020-05-13 Midlands 40
## 345 2020-05-14 Midlands 37
## 346 2020-05-15 Midlands 40
## 347 2020-05-16 Midlands 34
## 348 2020-05-17 Midlands 31
## 349 2020-05-18 Midlands 35
## 350 2020-05-19 Midlands 34
## 351 2020-05-20 Midlands 36
## 352 2020-05-21 Midlands 32
## 353 2020-05-22 Midlands 27
## 354 2020-05-23 Midlands 34
## 355 2020-05-24 Midlands 20
## 356 2020-05-25 Midlands 26
## 357 2020-05-26 Midlands 33
## 358 2020-05-27 Midlands 29
## 359 2020-05-28 Midlands 28
## 360 2020-05-29 Midlands 20
## 361 2020-05-30 Midlands 21
## 362 2020-05-31 Midlands 22
## 363 2020-06-01 Midlands 20
## 364 2020-06-02 Midlands 22
## 365 2020-06-03 Midlands 24
## 366 2020-06-04 Midlands 16
## 367 2020-06-05 Midlands 21
## 368 2020-06-06 Midlands 20
## 369 2020-06-07 Midlands 17
## 370 2020-06-08 Midlands 16
## 371 2020-06-09 Midlands 18
## 372 2020-06-10 Midlands 15
## 373 2020-06-11 Midlands 13
## 374 2020-06-12 Midlands 12
## 375 2020-06-13 Midlands 6
## 376 2020-06-14 Midlands 18
## 377 2020-06-15 Midlands 12
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## 401 2020-07-09 Midlands 8
## 402 2020-07-10 Midlands 3
## 403 2020-07-11 Midlands 0
## 404 2020-07-12 Midlands 3
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## 412 2020-03-07 North East and Yorkshire 0
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## 414 2020-03-09 North East and Yorkshire 0
## 415 2020-03-10 North East and Yorkshire 0
## 416 2020-03-11 North East and Yorkshire 0
## 417 2020-03-12 North East and Yorkshire 0
## 418 2020-03-13 North East and Yorkshire 0
## 419 2020-03-14 North East and Yorkshire 0
## 420 2020-03-15 North East and Yorkshire 2
## 421 2020-03-16 North East and Yorkshire 3
## 422 2020-03-17 North East and Yorkshire 1
## 423 2020-03-18 North East and Yorkshire 2
## 424 2020-03-19 North East and Yorkshire 6
## 425 2020-03-20 North East and Yorkshire 5
## 426 2020-03-21 North East and Yorkshire 6
## 427 2020-03-22 North East and Yorkshire 7
## 428 2020-03-23 North East and Yorkshire 9
## 429 2020-03-24 North East and Yorkshire 8
## 430 2020-03-25 North East and Yorkshire 18
## 431 2020-03-26 North East and Yorkshire 21
## 432 2020-03-27 North East and Yorkshire 28
## 433 2020-03-28 North East and Yorkshire 35
## 434 2020-03-29 North East and Yorkshire 38
## 435 2020-03-30 North East and Yorkshire 64
## 436 2020-03-31 North East and Yorkshire 60
## 437 2020-04-01 North East and Yorkshire 67
## 438 2020-04-02 North East and Yorkshire 75
## 439 2020-04-03 North East and Yorkshire 100
## 440 2020-04-04 North East and Yorkshire 105
## 441 2020-04-05 North East and Yorkshire 92
## 442 2020-04-06 North East and Yorkshire 96
## 443 2020-04-07 North East and Yorkshire 102
## 444 2020-04-08 North East and Yorkshire 107
## 445 2020-04-09 North East and Yorkshire 111
## 446 2020-04-10 North East and Yorkshire 117
## 447 2020-04-11 North East and Yorkshire 98
## 448 2020-04-12 North East and Yorkshire 84
## 449 2020-04-13 North East and Yorkshire 94
## 450 2020-04-14 North East and Yorkshire 107
## 451 2020-04-15 North East and Yorkshire 96
## 452 2020-04-16 North East and Yorkshire 103
## 453 2020-04-17 North East and Yorkshire 88
## 454 2020-04-18 North East and Yorkshire 95
## 455 2020-04-19 North East and Yorkshire 88
## 456 2020-04-20 North East and Yorkshire 100
## 457 2020-04-21 North East and Yorkshire 76
## 458 2020-04-22 North East and Yorkshire 84
## 459 2020-04-23 North East and Yorkshire 63
## 460 2020-04-24 North East and Yorkshire 72
## 461 2020-04-25 North East and Yorkshire 69
## 462 2020-04-26 North East and Yorkshire 65
## 463 2020-04-27 North East and Yorkshire 65
## 464 2020-04-28 North East and Yorkshire 57
## 465 2020-04-29 North East and Yorkshire 69
## 466 2020-04-30 North East and Yorkshire 57
## 467 2020-05-01 North East and Yorkshire 64
## 468 2020-05-02 North East and Yorkshire 48
## 469 2020-05-03 North East and Yorkshire 40
## 470 2020-05-04 North East and Yorkshire 49
## 471 2020-05-05 North East and Yorkshire 40
## 472 2020-05-06 North East and Yorkshire 51
## 473 2020-05-07 North East and Yorkshire 45
## 474 2020-05-08 North East and Yorkshire 42
## 475 2020-05-09 North East and Yorkshire 44
## 476 2020-05-10 North East and Yorkshire 40
## 477 2020-05-11 North East and Yorkshire 29
## 478 2020-05-12 North East and Yorkshire 27
## 479 2020-05-13 North East and Yorkshire 28
## 480 2020-05-14 North East and Yorkshire 31
## 481 2020-05-15 North East and Yorkshire 32
## 482 2020-05-16 North East and Yorkshire 35
## 483 2020-05-17 North East and Yorkshire 26
## 484 2020-05-18 North East and Yorkshire 30
## 485 2020-05-19 North East and Yorkshire 27
## 486 2020-05-20 North East and Yorkshire 22
## 487 2020-05-21 North East and Yorkshire 33
## 488 2020-05-22 North East and Yorkshire 22
## 489 2020-05-23 North East and Yorkshire 18
## 490 2020-05-24 North East and Yorkshire 26
## 491 2020-05-25 North East and Yorkshire 21
## 492 2020-05-26 North East and Yorkshire 21
## 493 2020-05-27 North East and Yorkshire 22
## 494 2020-05-28 North East and Yorkshire 21
## 495 2020-05-29 North East and Yorkshire 25
## 496 2020-05-30 North East and Yorkshire 20
## 497 2020-05-31 North East and Yorkshire 20
## 498 2020-06-01 North East and Yorkshire 17
## 499 2020-06-02 North East and Yorkshire 23
## 500 2020-06-03 North East and Yorkshire 23
## 501 2020-06-04 North East and Yorkshire 17
## 502 2020-06-05 North East and Yorkshire 18
## 503 2020-06-06 North East and Yorkshire 21
## 504 2020-06-07 North East and Yorkshire 14
## 505 2020-06-08 North East and Yorkshire 11
## 506 2020-06-09 North East and Yorkshire 12
## 507 2020-06-10 North East and Yorkshire 19
## 508 2020-06-11 North East and Yorkshire 7
## 509 2020-06-12 North East and Yorkshire 9
## 510 2020-06-13 North East and Yorkshire 10
## 511 2020-06-14 North East and Yorkshire 11
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## 526 2020-06-29 North East and Yorkshire 2
## 527 2020-06-30 North East and Yorkshire 4
## 528 2020-07-01 North East and Yorkshire 1
## 529 2020-07-02 North East and Yorkshire 4
## 530 2020-07-03 North East and Yorkshire 3
## 531 2020-07-04 North East and Yorkshire 4
## 532 2020-07-05 North East and Yorkshire 2
## 533 2020-07-06 North East and Yorkshire 2
## 534 2020-07-07 North East and Yorkshire 3
## 535 2020-07-08 North East and Yorkshire 3
## 536 2020-07-09 North East and Yorkshire 0
## 537 2020-07-10 North East and Yorkshire 2
## 538 2020-07-11 North East and Yorkshire 1
## 539 2020-07-12 North East and Yorkshire 3
## 540 2020-07-13 North East and Yorkshire 0
## 541 2020-03-01 North West 0
## 542 2020-03-02 North West 0
## 543 2020-03-03 North West 0
## 544 2020-03-04 North West 0
## 545 2020-03-05 North West 1
## 546 2020-03-06 North West 0
## 547 2020-03-07 North West 0
## 548 2020-03-08 North West 1
## 549 2020-03-09 North West 0
## 550 2020-03-10 North West 0
## 551 2020-03-11 North West 0
## 552 2020-03-12 North West 2
## 553 2020-03-13 North West 3
## 554 2020-03-14 North West 1
## 555 2020-03-15 North West 4
## 556 2020-03-16 North West 2
## 557 2020-03-17 North West 4
## 558 2020-03-18 North West 6
## 559 2020-03-19 North West 7
## 560 2020-03-20 North West 10
## 561 2020-03-21 North West 11
## 562 2020-03-22 North West 13
## 563 2020-03-23 North West 15
## 564 2020-03-24 North West 21
## 565 2020-03-25 North West 21
## 566 2020-03-26 North West 29
## 567 2020-03-27 North West 36
## 568 2020-03-28 North West 28
## 569 2020-03-29 North West 46
## 570 2020-03-30 North West 67
## 571 2020-03-31 North West 52
## 572 2020-04-01 North West 86
## 573 2020-04-02 North West 96
## 574 2020-04-03 North West 95
## 575 2020-04-04 North West 98
## 576 2020-04-05 North West 102
## 577 2020-04-06 North West 100
## 578 2020-04-07 North West 135
## 579 2020-04-08 North West 127
## 580 2020-04-09 North West 119
## 581 2020-04-10 North West 117
## 582 2020-04-11 North West 138
## 583 2020-04-12 North West 125
## 584 2020-04-13 North West 129
## 585 2020-04-14 North West 131
## 586 2020-04-15 North West 114
## 587 2020-04-16 North West 135
## 588 2020-04-17 North West 98
## 589 2020-04-18 North West 113
## 590 2020-04-19 North West 71
## 591 2020-04-20 North West 83
## 592 2020-04-21 North West 76
## 593 2020-04-22 North West 86
## 594 2020-04-23 North West 85
## 595 2020-04-24 North West 66
## 596 2020-04-25 North West 66
## 597 2020-04-26 North West 55
## 598 2020-04-27 North West 54
## 599 2020-04-28 North West 57
## 600 2020-04-29 North West 63
## 601 2020-04-30 North West 59
## 602 2020-05-01 North West 45
## 603 2020-05-02 North West 56
## 604 2020-05-03 North West 55
## 605 2020-05-04 North West 48
## 606 2020-05-05 North West 48
## 607 2020-05-06 North West 44
## 608 2020-05-07 North West 49
## 609 2020-05-08 North West 42
## 610 2020-05-09 North West 31
## 611 2020-05-10 North West 42
## 612 2020-05-11 North West 35
## 613 2020-05-12 North West 38
## 614 2020-05-13 North West 25
## 615 2020-05-14 North West 26
## 616 2020-05-15 North West 33
## 617 2020-05-16 North West 32
## 618 2020-05-17 North West 24
## 619 2020-05-18 North West 31
## 620 2020-05-19 North West 35
## 621 2020-05-20 North West 27
## 622 2020-05-21 North West 27
## 623 2020-05-22 North West 26
## 624 2020-05-23 North West 31
## 625 2020-05-24 North West 26
## 626 2020-05-25 North West 31
## 627 2020-05-26 North West 27
## 628 2020-05-27 North West 27
## 629 2020-05-28 North West 28
## 630 2020-05-29 North West 20
## 631 2020-05-30 North West 19
## 632 2020-05-31 North West 13
## 633 2020-06-01 North West 12
## 634 2020-06-02 North West 27
## 635 2020-06-03 North West 22
## 636 2020-06-04 North West 22
## 637 2020-06-05 North West 16
## 638 2020-06-06 North West 26
## 639 2020-06-07 North West 20
## 640 2020-06-08 North West 23
## 641 2020-06-09 North West 17
## 642 2020-06-10 North West 16
## 643 2020-06-11 North West 16
## 644 2020-06-12 North West 11
## 645 2020-06-13 North West 10
## 646 2020-06-14 North West 15
## 647 2020-06-15 North West 16
## 648 2020-06-16 North West 15
## 649 2020-06-17 North West 13
## 650 2020-06-18 North West 13
## 651 2020-06-19 North West 7
## 652 2020-06-20 North West 11
## 653 2020-06-21 North West 7
## 654 2020-06-22 North West 11
## 655 2020-06-23 North West 13
## 656 2020-06-24 North West 13
## 657 2020-06-25 North West 15
## 658 2020-06-26 North West 6
## 659 2020-06-27 North West 7
## 660 2020-06-28 North West 9
## 661 2020-06-29 North West 7
## 662 2020-06-30 North West 6
## 663 2020-07-01 North West 3
## 664 2020-07-02 North West 6
## 665 2020-07-03 North West 6
## 666 2020-07-04 North West 4
## 667 2020-07-05 North West 6
## 668 2020-07-06 North West 9
## 669 2020-07-07 North West 7
## 670 2020-07-08 North West 5
## 671 2020-07-09 North West 10
## 672 2020-07-10 North West 2
## 673 2020-07-11 North West 3
## 674 2020-07-12 North West 0
## 675 2020-07-13 North West 2
## 676 2020-03-01 South East 0
## 677 2020-03-02 South East 0
## 678 2020-03-03 South East 1
## 679 2020-03-04 South East 0
## 680 2020-03-05 South East 1
## 681 2020-03-06 South East 0
## 682 2020-03-07 South East 0
## 683 2020-03-08 South East 1
## 684 2020-03-09 South East 1
## 685 2020-03-10 South East 1
## 686 2020-03-11 South East 1
## 687 2020-03-12 South East 0
## 688 2020-03-13 South East 1
## 689 2020-03-14 South East 1
## 690 2020-03-15 South East 5
## 691 2020-03-16 South East 8
## 692 2020-03-17 South East 7
## 693 2020-03-18 South East 10
## 694 2020-03-19 South East 9
## 695 2020-03-20 South East 13
## 696 2020-03-21 South East 7
## 697 2020-03-22 South East 25
## 698 2020-03-23 South East 20
## 699 2020-03-24 South East 22
## 700 2020-03-25 South East 29
## 701 2020-03-26 South East 35
## 702 2020-03-27 South East 34
## 703 2020-03-28 South East 36
## 704 2020-03-29 South East 55
## 705 2020-03-30 South East 58
## 706 2020-03-31 South East 65
## 707 2020-04-01 South East 66
## 708 2020-04-02 South East 55
## 709 2020-04-03 South East 72
## 710 2020-04-04 South East 80
## 711 2020-04-05 South East 82
## 712 2020-04-06 South East 88
## 713 2020-04-07 South East 100
## 714 2020-04-08 South East 83
## 715 2020-04-09 South East 104
## 716 2020-04-10 South East 88
## 717 2020-04-11 South East 88
## 718 2020-04-12 South East 88
## 719 2020-04-13 South East 84
## 720 2020-04-14 South East 65
## 721 2020-04-15 South East 72
## 722 2020-04-16 South East 56
## 723 2020-04-17 South East 86
## 724 2020-04-18 South East 57
## 725 2020-04-19 South East 70
## 726 2020-04-20 South East 87
## 727 2020-04-21 South East 51
## 728 2020-04-22 South East 54
## 729 2020-04-23 South East 57
## 730 2020-04-24 South East 64
## 731 2020-04-25 South East 51
## 732 2020-04-26 South East 51
## 733 2020-04-27 South East 40
## 734 2020-04-28 South East 40
## 735 2020-04-29 South East 47
## 736 2020-04-30 South East 29
## 737 2020-05-01 South East 37
## 738 2020-05-02 South East 36
## 739 2020-05-03 South East 17
## 740 2020-05-04 South East 35
## 741 2020-05-05 South East 29
## 742 2020-05-06 South East 25
## 743 2020-05-07 South East 27
## 744 2020-05-08 South East 26
## 745 2020-05-09 South East 28
## 746 2020-05-10 South East 19
## 747 2020-05-11 South East 25
## 748 2020-05-12 South East 27
## 749 2020-05-13 South East 18
## 750 2020-05-14 South East 32
## 751 2020-05-15 South East 25
## 752 2020-05-16 South East 22
## 753 2020-05-17 South East 18
## 754 2020-05-18 South East 22
## 755 2020-05-19 South East 12
## 756 2020-05-20 South East 22
## 757 2020-05-21 South East 15
## 758 2020-05-22 South East 17
## 759 2020-05-23 South East 21
## 760 2020-05-24 South East 17
## 761 2020-05-25 South East 13
## 762 2020-05-26 South East 19
## 763 2020-05-27 South East 18
## 764 2020-05-28 South East 12
## 765 2020-05-29 South East 21
## 766 2020-05-30 South East 8
## 767 2020-05-31 South East 12
## 768 2020-06-01 South East 11
## 769 2020-06-02 South East 13
## 770 2020-06-03 South East 18
## 771 2020-06-04 South East 11
## 772 2020-06-05 South East 11
## 773 2020-06-06 South East 10
## 774 2020-06-07 South East 12
## 775 2020-06-08 South East 8
## 776 2020-06-09 South East 10
## 777 2020-06-10 South East 11
## 778 2020-06-11 South East 5
## 779 2020-06-12 South East 6
## 780 2020-06-13 South East 6
## 781 2020-06-14 South East 7
## 782 2020-06-15 South East 8
## 783 2020-06-16 South East 13
## 784 2020-06-17 South East 9
## 785 2020-06-18 South East 4
## 786 2020-06-19 South East 7
## 787 2020-06-20 South East 5
## 788 2020-06-21 South East 3
## 789 2020-06-22 South East 2
## 790 2020-06-23 South East 8
## 791 2020-06-24 South East 7
## 792 2020-06-25 South East 5
## 793 2020-06-26 South East 8
## 794 2020-06-27 South East 8
## 795 2020-06-28 South East 6
## 796 2020-06-29 South East 5
## 797 2020-06-30 South East 5
## 798 2020-07-01 South East 2
## 799 2020-07-02 South East 7
## 800 2020-07-03 South East 3
## 801 2020-07-04 South East 5
## 802 2020-07-05 South East 4
## 803 2020-07-06 South East 3
## 804 2020-07-07 South East 5
## 805 2020-07-08 South East 3
## 806 2020-07-09 South East 7
## 807 2020-07-10 South East 3
## 808 2020-07-11 South East 0
## 809 2020-07-12 South East 2
## 810 2020-07-13 South East 1
## 811 2020-03-01 South West 0
## 812 2020-03-02 South West 0
## 813 2020-03-03 South West 0
## 814 2020-03-04 South West 0
## 815 2020-03-05 South West 0
## 816 2020-03-06 South West 0
## 817 2020-03-07 South West 0
## 818 2020-03-08 South West 0
## 819 2020-03-09 South West 0
## 820 2020-03-10 South West 0
## 821 2020-03-11 South West 1
## 822 2020-03-12 South West 0
## 823 2020-03-13 South West 0
## 824 2020-03-14 South West 1
## 825 2020-03-15 South West 0
## 826 2020-03-16 South West 0
## 827 2020-03-17 South West 2
## 828 2020-03-18 South West 2
## 829 2020-03-19 South West 4
## 830 2020-03-20 South West 3
## 831 2020-03-21 South West 6
## 832 2020-03-22 South West 7
## 833 2020-03-23 South West 8
## 834 2020-03-24 South West 7
## 835 2020-03-25 South West 9
## 836 2020-03-26 South West 11
## 837 2020-03-27 South West 13
## 838 2020-03-28 South West 21
## 839 2020-03-29 South West 18
## 840 2020-03-30 South West 23
## 841 2020-03-31 South West 23
## 842 2020-04-01 South West 21
## 843 2020-04-02 South West 23
## 844 2020-04-03 South West 30
## 845 2020-04-04 South West 42
## 846 2020-04-05 South West 32
## 847 2020-04-06 South West 34
## 848 2020-04-07 South West 39
## 849 2020-04-08 South West 47
## 850 2020-04-09 South West 24
## 851 2020-04-10 South West 46
## 852 2020-04-11 South West 43
## 853 2020-04-12 South West 23
## 854 2020-04-13 South West 27
## 855 2020-04-14 South West 24
## 856 2020-04-15 South West 32
## 857 2020-04-16 South West 29
## 858 2020-04-17 South West 33
## 859 2020-04-18 South West 25
## 860 2020-04-19 South West 31
## 861 2020-04-20 South West 26
## 862 2020-04-21 South West 26
## 863 2020-04-22 South West 23
## 864 2020-04-23 South West 17
## 865 2020-04-24 South West 19
## 866 2020-04-25 South West 15
## 867 2020-04-26 South West 27
## 868 2020-04-27 South West 13
## 869 2020-04-28 South West 17
## 870 2020-04-29 South West 15
## 871 2020-04-30 South West 26
## 872 2020-05-01 South West 6
## 873 2020-05-02 South West 7
## 874 2020-05-03 South West 10
## 875 2020-05-04 South West 17
## 876 2020-05-05 South West 14
## 877 2020-05-06 South West 19
## 878 2020-05-07 South West 16
## 879 2020-05-08 South West 6
## 880 2020-05-09 South West 11
## 881 2020-05-10 South West 5
## 882 2020-05-11 South West 8
## 883 2020-05-12 South West 7
## 884 2020-05-13 South West 7
## 885 2020-05-14 South West 6
## 886 2020-05-15 South West 4
## 887 2020-05-16 South West 4
## 888 2020-05-17 South West 6
## 889 2020-05-18 South West 4
## 890 2020-05-19 South West 6
## 891 2020-05-20 South West 1
## 892 2020-05-21 South West 9
## 893 2020-05-22 South West 6
## 894 2020-05-23 South West 6
## 895 2020-05-24 South West 3
## 896 2020-05-25 South West 8
## 897 2020-05-26 South West 11
## 898 2020-05-27 South West 5
## 899 2020-05-28 South West 10
## 900 2020-05-29 South West 7
## 901 2020-05-30 South West 3
## 902 2020-05-31 South West 2
## 903 2020-06-01 South West 7
## 904 2020-06-02 South West 2
## 905 2020-06-03 South West 7
## 906 2020-06-04 South West 2
## 907 2020-06-05 South West 2
## 908 2020-06-06 South West 1
## 909 2020-06-07 South West 3
## 910 2020-06-08 South West 3
## 911 2020-06-09 South West 0
## 912 2020-06-10 South West 1
## 913 2020-06-11 South West 2
## 914 2020-06-12 South West 2
## 915 2020-06-13 South West 2
## 916 2020-06-14 South West 0
## 917 2020-06-15 South West 2
## 918 2020-06-16 South West 2
## 919 2020-06-17 South West 0
## 920 2020-06-18 South West 0
## 921 2020-06-19 South West 0
## 922 2020-06-20 South West 2
## 923 2020-06-21 South West 0
## 924 2020-06-22 South West 1
## 925 2020-06-23 South West 1
## 926 2020-06-24 South West 1
## 927 2020-06-25 South West 0
## 928 2020-06-26 South West 3
## 929 2020-06-27 South West 0
## 930 2020-06-28 South West 0
## 931 2020-06-29 South West 1
## 932 2020-06-30 South West 0
## 933 2020-07-01 South West 0
## 934 2020-07-02 South West 0
## 935 2020-07-03 South West 0
## 936 2020-07-04 South West 0
## 937 2020-07-05 South West 1
## 938 2020-07-06 South West 0
## 939 2020-07-07 South West 0
## 940 2020-07-08 South West 2
## 941 2020-07-09 South West 0
## 942 2020-07-10 South West 1
## 943 2020-07-11 South West 0
## 944 2020-07-12 South West 0
## 945 2020-07-13 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 14 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -12.9754 -4.0837 -0.1693 3.6430 7.9926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.623e+00 6.411e-02 72.11 <2e-16 ***
## note_lag 1.431e-05 6.655e-07 21.50 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 23.10141)
##
## Null deviance: 11592.0 on 73 degrees of freedom
## Residual deviance: 1766.8 on 72 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 101.824221 1.000014
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 89.625660 115.240670
## note_lag 1.000013 1.000016
Rsq(lag_mod)
## [1] 0.8475861
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.1
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.1 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1